JP2012247854A - Driving-evaluation system, driving-evaluating program, and driving-evaluation method - Google Patents

Abstract

PROBLEM TO BE SOLVED: To improve appropriateness in evaluation of how a driver drives a vehicle.SOLUTION: A driving-evaluation system 100 for evaluating how a driver drives a vehicle on the basis of travel behave data, such as a vehicle speed and acceleration, collected by a data recorder includes: a driving-action extraction part 12 for extracting a driving action on the basis of the travel behave data read from a memory medium 40 by a memory control part 51; a numerical value extraction part 14 for extracting a numerical value being the travel behave data related to a driving action, the data extracted by the driving-action extraction part 12; a database 18 for recording evaluation target data related to plural drivers including an evaluation-target driver, the data extracted by the numerical value extraction part 14; and a driving-evaluation part 20 for calculating distribution of the evaluation target data related to plural drivers, the data recorded in the database 18, and evaluating driving of the evaluation-target driver according to a predetermined evaluation criterion based on the relation between the distribution and the average value of the evaluation-target data related to the evaluation-target driver.

Description

  The present invention relates to a driving evaluation system, a driving evaluation program, and a driving evaluation method for evaluating driving of a vehicle driver.

  In-vehicle / always-on-recording that constantly records in the memory medium the angular velocity and acceleration data measured by the gyro sensor and acceleration sensor, and the vehicle speed and position data measured by the GPS according to the running behavior of the vehicle. A type of data recorder is known (see, for example, Patent Document 1). Also, based on the angular velocity, acceleration, velocity data, etc. recorded in the data recorder, feature information that is characteristic of the driver's steering operation, brake operation, accelerator operation, and other driving operations is generated. There is known a driving diagnosis device that generates diagnostic information for diagnosing the driving technique of the driver based on the safety standard data (see, for example, Patent Document 1). In this driving diagnosis device, the average value of acceleration, etc. at the time of steering wheel operation and braking operation, or a value obtained by multiplying this by a deviation is compared with an ideal value such as actual data by a model driver as safety reference data. Quantify by

Japanese Patent Laid-Open No. 2002-2111265

  However, for example, driving at a slow speed is generally safe driving, but driving at a slow speed in a driving area where the average speed is fast is dangerous driving. Is relative. Therefore, as in the driving diagnosis device described in Patent Document 1, the driving is evaluated based on a relative evaluation criterion that takes into account the relativity with other drivers, rather than evaluating the driving based on an absolute evaluation criterion. Can make a more appropriate assessment.

  In view of the above circumstances, the present invention provides a driving evaluation system, a driving evaluation program, and a driving evaluation method capable of more appropriately evaluating driving of a vehicle driver, particularly driving ability without causing an accident. It is to be an issue.

  In order to solve the above problems, a driving evaluation system according to the present invention is based on driving behavior data collected in a time series during driving of a vehicle to identify the driving behavior of the vehicle. A driving evaluation system for evaluating driving of a vehicle, wherein a data acquisition unit for acquiring the driving behavior data and a driving behavior specification for specifying a driving behavior of a vehicle based on the driving behavior data acquired by the data acquisition unit A data extraction unit that extracts evaluation target data that is the driving behavior data related to the driving behavior of the vehicle specified by the driving behavior specifying unit, and a driver to be evaluated extracted by the data extraction unit. A data recording unit for recording the evaluation target data for a plurality of drivers, and the evaluation target data for the plurality of drivers recorded in the data recording unit. Statistical data is calculated, and based on the relationship between the statistical data and the evaluation target data for the evaluation target driver recorded in the data recording unit, the evaluation target driver according to a predetermined evaluation criterion A driving evaluation unit for evaluating the driving.

  In the driving evaluation system, the data acquisition unit may acquire travel area data for identifying a travel area of the vehicle collected in time series together with the travel behavior data during operation of the vehicle, The driving evaluation unit calculates a driving ratio of the driving area of the driver to be evaluated from the driving area data acquired by the data acquisition unit, and sets the predetermined evaluation standard according to the calculated driving ratio. And an evaluation value calculating unit that calculates an evaluation value based on the predetermined evaluation criterion set by the evaluation criterion setting unit.

  In the driving evaluation system, the evaluation criterion setting unit, when the driver to be evaluated is a driver who has caused a traffic accident, evaluates the driver recorded in the evaluation target data recording unit. The predetermined evaluation criterion may be set based on target data.

  In the driving evaluation system, the evaluation criterion setting unit may set a weight value used when the evaluation value calculation unit calculates an evaluation value when setting the predetermined evaluation criterion.

  In the driving evaluation system, the evaluation criterion setting unit sets the predetermined evaluation criterion for each evaluation item set for each of a plurality of driving actions, and the evaluation value calculation unit includes a plurality of the evaluation values. May be calculated for each evaluation item set for each driving action, and the driving evaluation unit totals the evaluation values of the plurality of evaluation items calculated by the evaluation value calculation unit, and the total value And an evaluation value totaling unit for scoring the driving of the driver to be evaluated.

  In the driving evaluation system, the driving evaluation unit includes a pass / fail determination unit that determines pass / fail of each driving action of the driver to be evaluated based on the predetermined evaluation criterion, and a driving action that is negatively determined by the pass / fail determination unit. A ratio calculation unit that calculates a ratio to all driving actions.

  Further, the driving evaluation program according to the present invention evaluates the driving of the driver of the vehicle based on the driving behavior data for identifying the driving behavior of the vehicle collected in time series during the operation of the vehicle. A driving evaluation program for causing a computer to realize a function for the above, a data acquisition function for acquiring the driving behavior data, and a driving behavior of the vehicle based on the driving behavior data acquired by the data acquisition function A data extraction function for extracting evaluation target data, which is the driving behavior data related to the driving behavior of the vehicle specified by the driving behavior specifying function, and the data extracting function. A data recording function for recording the evaluation target data for a plurality of drivers including the driver to be evaluated, and the data recording function. Statistical data of the evaluation target data for the plurality of drivers is calculated, and predetermined based on the relationship between the evaluation target data for the evaluation target driver recorded by the data recording function and the statistical data A driving evaluation program for causing a computer to realize a driving evaluation function for evaluating the driving of the driver to be evaluated according to the evaluation criteria.

  In addition, the driving evaluation method according to the present invention evaluates the driving of the driver of the vehicle based on the driving behavior data for identifying the driving behavior of the vehicle collected in time series during the operation of the vehicle. A driving evaluation method for executing the above procedure on a computer, wherein the driving behavior data is acquired, and the driving behavior of the vehicle is specified based on the driving behavior data acquired in the data acquisition procedure. A behavior identification procedure, a data extraction procedure for extracting evaluation target data that is the travel behavior data related to the driving behavior of the vehicle identified in the driving behavior identification procedure, and the evaluation target driving extracted in the data extraction procedure A data recording procedure for recording the evaluation object data for a plurality of drivers including a driver in a recording unit, and the plurality of drivers recorded by the data recording procedure. The statistical data of the evaluation target data is calculated, and the evaluation is performed according to a predetermined evaluation criterion based on the relationship between the evaluation target data and the statistical data for the evaluation target driver recorded by the data recording procedure. This is a driving evaluation method in which a driving evaluation procedure for evaluating driving of a target driver is executed by a computer.

  According to the present invention, it is possible to provide a driving evaluation system, a driving evaluation program, and a driving evaluation method that can more appropriately evaluate driving of a vehicle driver, particularly driving ability that does not cause an accident.

It is a block diagram showing a schematic structure of a driving evaluation system concerning one embodiment. It is a block diagram which shows the structure of a driving | running evaluation apparatus. It is a graph which shows the speed data and the acceleration data of the front-back direction at the time of a driving action extraction part extracting "start" and "start acceleration" as a driving action. It is a graph which shows the speed data and the acceleration data of the front-back direction when a driving action extraction part extracts "acceleration start", "accelerating", "acceleration time", and "acceleration" as a driving action. A graph showing the speed data and the acceleration data in the front-rear direction when the driving action extraction unit extracts “deceleration start”, “decelerating”, “deceleration time”, “deceleration acceleration”, “stop” as driving actions is there. It is a graph which shows the speed data, the right-and-left acceleration data, turning angular velocity data, and turning angle data at the time of a driving action extraction part extracting "right turning" as a driving action. It is a graph which shows the speed data, the right-and-left acceleration data, turning angular velocity data, and turning angle when the driving action extraction unit extracts “while turning left” as the driving action. It is a graph which shows the speed data, the right-and-left acceleration data, the turning angular velocity data, and the turning angle data when the driving action extraction unit extracts “changing right course” as the driving action. It is a graph which shows the speed data, the right-and-left acceleration data, turning angular velocity data, and turning angle data at the time of a driving action extraction part extracting "the left way change in progress" as a driving action. It is the table | surface which put together the item extracted by a numerical value extraction part. It is a functional block diagram of a driving evaluation part. It is the table | surface which put together the evaluation item and the derivation method of evaluation object data. It is a graph which shows the reference data used when an evaluation point calculation part evaluates five steps about each evaluation item. It is a flowchart for demonstrating the update method of the weight by an evaluation criteria setting part. It is a graph which shows the speed and longitudinal acceleration just before a collision. It is a figure which shows the form of the result about the safe driving technique of the driver of evaluation object. It is a functional block diagram of the driving evaluation part concerning other embodiments. It is a figure which shows the calculation method of a stability factor typically. It is a block diagram which shows schematic structure of the driving | operation evaluation system which concerns on other embodiment. It is a block diagram which shows the structure of the driving | running evaluation apparatus which concerns on other embodiment.

  Hereinafter, an embodiment of the present invention will be described with reference to the drawings. FIG. 1 is a block diagram illustrating a schematic configuration of a driving evaluation system 100 according to an embodiment. The driving evaluation system 100 includes a driving evaluation device 10, a constantly-recording data recorder 30 mounted on a vehicle such as a truck, and a stick-type or card-type memory medium 40 that is attached to or detached from the data recorder 30. .

  The data recorder 30 includes a sensor unit 32 that measures vehicle speed, acceleration, position, and the like (travel behavior data), and a recorder unit 34 that records data measured by the sensor unit 32 in the memory medium 40. ing. The sensor unit 32 includes an angular velocity sensor 321 such as a gyro sensor, a geomagnetic sensor, a GPS azimuth sensor, a lateral acceleration sensor 322, a longitudinal acceleration sensor 323, a GPS receiver 324, and a vehicle speed pulse acquisition unit 325.

  The angular velocity sensor 321 measures angular velocities such as the roll rate, pitch rate, and yaw rate of the vehicle. Further, the lateral acceleration sensor 322 measures the acceleration in the vehicle lateral direction. The longitudinal acceleration sensor 323 measures acceleration in the longitudinal direction of the vehicle. The GPS receiver 324 receives GPS data representing the current latitude / longitude / direction of the vehicle, the current time / weather, and the like. Furthermore, the vehicle speed pulse acquisition unit 325 acquires a vehicle speed pulse from a vehicle measuring instrument or the like.

  Here, the sensor unit 32 outputs GPS data when the GPS receiver 324 receives GPS data, and acquires a vehicle speed pulse by the vehicle speed pulse acquisition unit 325 when the GPS receiver 324 does not receive GPS data. The vehicle speed pulse is output. For example, when the vehicle is traveling on a road where GPS data can be received, the sensor unit 32 outputs GPS data as measurement data representing vehicle speed, and when the vehicle is traveling in a tunnel where GPS data cannot be received, A vehicle speed pulse is output as measurement data representing the vehicle speed.

  The recorder unit 34 includes a preprocessing unit 341, a data recording unit 342, and a buffer memory 343, and executes processing of each functional block at a predetermined cycle (for example, 1 Hz). The preprocessing unit 341 temporarily records measurement data (running behavior data) that is waveform data output from the sensor unit 32 in the buffer memory 343, and performs processing for removing the offset component and drift component of the recorded angular velocity data. Execute. In addition, the preprocessing unit 341 executes matching processing between angular velocity data, acceleration data, and GPS data. Here, since the GPS data is received by the GPS receiver 324 with a delay of several seconds with respect to the timing at which the angular velocity data and the acceleration data are measured, the preprocessing unit 341 includes the GPS data, the angular velocity data and the acceleration data of several seconds before. Execute the matching process.

  The data recording unit 342 records the angular velocity data, acceleration data, and GPS data on which the processing by the preprocessing unit 341 has been performed, in the memory medium 40. Here, the data recording unit 342 includes angular velocity data measured by the angular velocity sensor 321, vehicle lateral acceleration data measured by the lateral acceleration sensor 322, vehicle longitudinal acceleration data measured by the longitudinal acceleration sensor 323, GPS The speed data received by the receiver 324 or acquired by the vehicle speed pulse acquisition unit 325 is recorded in the memory medium 40 in association with information such as the latitude / longitude / direction of the vehicle, time / weather received by the GPS receiver 324.

  The memory medium 40 is a non-volatile semiconductor memory, and is detachable from the data recorder 30 and detachable from a general-purpose computer. In this memory medium 40, driver identification data (hereinafter referred to as driver ID), a management data recording area in which vehicle identification data and the like are recorded, and measurement data which is waveform data measured by the sensor unit 32 are stored. A measurement data recording area to be recorded is provided.

  The driving evaluation apparatus 10 is a computer system to which the memory medium 40 is attached and detached, and is operated by a driving evaluation program stored in a hard disk of a personal computer or provided by an application service provider (ASP) or cloud computing. Thus, the technology relating to the safe driving of the driver identified by the driver ID (hereinafter referred to as the driver to be evaluated) is evaluated.

  FIG. 2 is a block diagram illustrating a configuration of the driving evaluation device 10. As shown in this figure, the driving evaluation apparatus 10 includes a memory control unit 51 that reads data from the memory medium 40 and writes data to the memory medium 40, and a keyboard for inputting data, commands, and the like. Input device 52, a display device 53 such as an LCD for displaying an operation screen, evaluation results, and the like, and a main control unit 11 in which various functions are realized by the operation evaluation program. The main control unit 11 includes a driving action extraction unit 12, a numerical value extraction unit 14, a data recording unit 16, a database 18, a driving evaluation unit 20, and an evaluation information management unit 22.

  The driving action extraction unit 12 extracts (specifies) seven types of driving actions (running behavior) such as start, acceleration, deceleration, stop, right turn, left turn, and course change from measurement data such as acceleration recorded in the memory medium 40. Execute the process. Here, the driving action extraction unit 12 extracts the corresponding driving action when the predetermined measurement data exceeds a predetermined threshold. Note that the predetermined threshold is determined by analyzing waveform data of actual travel records collected in the past, and will be described in detail later.

  The numerical value extraction unit 14 extracts a plurality of items related to the driving action extracted by the driving action extraction unit 12. Here, each item extracted by the numerical value extraction unit 14 is expressed by numerical values such as speed and acceleration, as will be described later.

  The data recording unit 16 reads the driver ID from the memory medium 40 and links the data extracted by the numerical value extraction unit 14 (hereinafter referred to as evaluation target data) and the driver ID and records them in the database 18. Here, the database 18 stores not only evaluation target data for the driver to be evaluated but also evaluation target data for all the drivers that have been evaluated so far.

  The driving evaluation unit 20 calculates statistical data such as the distribution of the evaluation target data of all the drivers recorded in the database 18, and performs predetermined processing based on the statistical data and the evaluation target data of the evaluation target driver. Based on the evaluation criteria, the driver's safe driving skills are evaluated. Here, the driving evaluation unit 20 calculates the distribution and variance of the evaluation target data of other drivers necessary for the evaluation for each evaluation item described later, and averages the evaluation target data of the evaluation target driver. A value is obtained, and a five-step evaluation is performed according to a predetermined evaluation criterion based on these relationships. And the technique of the safe driving | operation of the driver | operator of evaluation object is scored by adding the score of all the evaluation items.

  The evaluation information management unit 22 records evaluation result data from the driving evaluation unit 20 in the database 18. Further, the evaluation information management unit 22 creates data for creating a form that summarizes the results of the safe driving of the driver to be evaluated.

Here, with reference to FIG. 3 to FIG. 9, a driving action extraction method by the driving action extraction unit 12 will be described. FIG. 3 is a graph showing speed data and acceleration data in the front-rear direction when the driving action extraction unit 12 extracts “start” and “start acceleration” as driving actions. As shown in this graph, the driving action extraction unit 12 has a state in which the speed for 5 seconds before a certain time t ₁ is 0 km / h and the speed is not 0 km / h after the time t ₁ for 5 seconds or more. When the operation is continued, “start” is extracted as the driving action at the time t ₁ . Further, the driving action extraction unit 12 performs a simple moving average of the preceding and following data (data of time t _i-1 and time t _i , the same applies hereinafter) for the acceleration in the front-rear direction for 5 seconds after the start, and the value And the maximum value is extracted as “starting acceleration”. In addition, the driving action extracting unit 12 performs an action from time t ₁ at which “start” is extracted to time t ₂ until a value obtained by simply moving and averaging forward and backward data with respect to acceleration in the forward direction falls below + 0.08G. As “acceleration” is extracted. Further, the driving action extraction unit 12 extracts the time (t ₂ -t ₁ ) during the determination that “acceleration is in progress” as “acceleration time”.

FIG. 4 is a graph showing speed data and acceleration data in the front-rear direction when the driving action extracting unit 12 extracts “acceleration start”, “accelerating”, “acceleration time”, and “acceleration” as driving actions. is there. In the following description, the forward acceleration is described as + ◯ G, and the backward acceleration (that is, deceleration acceleration) is described as − ◯ G. As shown in the graph of FIG. 4, the driving action extraction unit 12 has a value obtained by simply moving and averaging the previous and subsequent data for the acceleration in the front-rear direction when the speed is not 0 km / h. Is exceeded, “acceleration start” is extracted as the action at time t ₁ . Then, the operation acts extraction unit 12, from the "acceleration start" time t ₁ obtained by extracting the simple moving average value of the data before and after the acceleration in the forward direction between up to time t ₂ to below + 0.08 g As an action, “accelerating” is extracted. Further, the driving action extraction unit 12 extracts the time (t ₂ -t ₁ ) during which it is determined that “acceleration is in progress” as “acceleration time”, and is a value obtained by simply moving and averaging the data before and after the acceleration during that time And the maximum value is extracted as “acceleration”.

FIG. 5 shows speed data when the driving action extraction unit 12 extracts “deceleration start”, “decelerating”, “deceleration time”, “deceleration acceleration”, and “stop” as driving actions, acceleration in the front-rear direction, It is a graph which shows data. As shown in this graph, the driving action extraction unit 12 is a case where the state where the speed is not 0 km / h continues, and the value obtained by simply moving and averaging the previous and subsequent data regarding the acceleration in the forward direction is −0. If it falls below the 08G, to extract the "deceleration start" as an act of that time t _1. Then, the driving action extracting unit 12 extracts “deceleration start” and then acts as “being decelerating” as an action until the value obtained by simply moving and averaging the preceding and following data with respect to the acceleration in the forward direction exceeds −0.08G. Is extracted. Further, the driving action extraction unit 12 extracts the time during which “deceleration” is determined as “deceleration time”, monitors a value obtained by simply moving and averaging the data before and after the acceleration, and calculates the absolute value thereof. The maximum value is extracted as “deceleration acceleration”.

The driving action extraction unit 12 extracts “stop” as the action at the time t ₃ when the speed at that time falls below 4 km / h for the action determined to be “decelerated”.

  FIG. 6 is a graph showing speed data, left / right acceleration data, turning angular velocity data, and turning angle data when the driving action extraction unit 12 extracts “mightening right” as the driving action. In the following description, the acceleration in the left direction is described as + OG, and the acceleration in the right direction is described as -OG. Further, the turning angular velocity in the right direction is described as + ° / sec, and the turning angular velocity in the left direction is described as − ◯ ° / sec. Furthermore, the turning angle in the right direction is described as + °, and the turning angle in the left direction is described as − ◯ °.

As shown in the graph of FIG. 6, the driving action extraction unit 12 determines the turning angular velocity at time t ₁ when the lateral acceleration exceeds +0.08 G while the speed is not 0 km / h. in case where but + 8 ° / sec or more, when the turning angle at the time t ₂ when the acceleration in the lateral direction is below + 0.08 g is + 15 ° or more, the "in turn right" as an act at the time t ₂ Extract.

FIG. 7 is a graph showing speed data, left and right acceleration data, turning angular velocity data, and turning angle data when the driving action extraction unit 12 extracts “while turning left” as the driving action. As shown in this graph, the driving action extracting unit 12 has the turning angular velocity at time t ₁ when the acceleration in the left-right direction is less than -0.08G while the speed is not 0 km / h. When the turning angle is −15 ° or less at time t ₂ when the lateral acceleration exceeds −0.08 G when −8 ° / sec or less, the action at time t ₂ is “ Is extracted.

FIG. 8 is a graph showing speed data, left and right acceleration data, turning angular velocity data, and turning angle data when the driving action extracting unit 12 extracts “changing rightward route” as the driving action. As shown in this graph, the driving action extraction unit 12 has a turning angular velocity of +4 at time t ₁ when the lateral acceleration exceeds +0.08 G while the speed is not 0 km / h. in ° / sec or more in the case of, after the acceleration in the lateral direction from the time _{t 1} within 5 seconds is below -0.08G, turning at the time _{t 2} when _the acceleration in the left and right direction exceeds -0.08G angle There when a + 4 ° or less (0 ° or more), to extract the "right middle course change" as an act of the time t _2.

FIG. 9 is a graph showing speed data, left / right acceleration data, turning angular velocity data, and turning angle data when the driving action extracting unit 12 extracts “changing leftward course” as the driving action. As shown in this graph, the driving action extracting unit 12 has the turning angular velocity at time t ₁ when the acceleration in the left-right direction is less than -0.08G while the speed is not 0 km / h. In the case of −4 ° / sec or less, the turning angle at time t ₂ when the lateral acceleration exceeds +0.08 G within 5 seconds from time t ₁ and then the lateral acceleration falls below +0.08 G. Is -4 ° or more (0 ° or less), “changing left course” is extracted as the action at time t ₂ .

  After the driving action is extracted by the driving action extraction unit 12 as described above, the numerical value extraction unit 14 extracts a plurality of items related to the driving action extracted by the driving action extraction unit 12. Each item extracted here is represented by a numerical value as follows.

  FIG. 10 is a table summarizing items extracted by the numerical value extraction unit 14. As shown in this table, the numerical value extraction unit 14 includes, as items relating to right and left turns, (1) speed before action (km / h), (2) speed during action (km / h), and (3) during action The acceleration in the left-right direction (G), (4) the longitudinal acceleration (G) after the action, and (5) the speed (km / h) after the action are extracted. In addition, the numerical value extraction unit 14 includes (1) speed before the action (km / h), (2) left-right acceleration (G) during the action, and (3) front-back direction after the action as items related to the course change. Acceleration (G), (4) Speed after action (km / h) is extracted. Furthermore, the numerical value extraction unit 14 includes, as items relating to acceleration (start) and deceleration (stop), (1) speed before action (km / h), (2) longitudinal acceleration (G) during action, (3 ) Extract the speed (km / h) after the action.

  As described above, the values extracted before, during, and during each driving action such as acceleration reflect the concept of the traffic accident negligence rate in the evaluation of the driver's safe driving. When calculating the negligence rate, the driving action is broken down into processes, and it is clarified which process caused the accident (negligence). So, for example, if it is a right turn, it will cause an accident such as `` how much you decelerated before making a right turn '', `` how you turned the handle when turning right '', `` how you accelerated after turning right '' The evaluation is divided into related actions, so that the driver can understand his / her badness more specifically. The threshold for the driving action extraction unit 12 to extract the driving action is calculated by analyzing the numeric value extracted as described above.

  Here, a method of recording the evaluation target data in the database 18 by the data recording unit 16 will be described. In the database 18, the operation time, the travel time, and the travel distance for each specific area are recorded as evaluation target data regarding the operation time. Here, the travel distance for each specific area is a travel distance for each area divided into, for example, an urban area, a suburb, or a highway. Further, data for specifying the area (traveling area data) is position information received by the GPS receiver 24.

  In addition, the database 18 includes, as evaluation target data regarding right and left turns, left and right acceleration at the time of right turn, left and right direction acceleration at the time of left turn, speed immediately before the right turn, speed immediately before the left turn, longitudinal acceleration at the time of right turn, and front and rear at the time of left turn Directional acceleration is recorded. In addition, the database 18 records, as evaluation target data regarding the course change, the lateral acceleration at the course change, the longitudinal acceleration at the course change, the speed at the course change, and the travel area at the course change. Further, the database 18 records acceleration / deceleration longitudinal acceleration, acceleration / deceleration acceleration / deceleration acceleration, and acceleration / deceleration speed data as deceleration data, and deceleration / deceleration longitudinal / deceleration acceleration / deceleration travel. Area, speed at deceleration is recorded. Further, the speed for each specific area is recorded in the database 18. Here, the speed for each specific area is, for example, the speed for each divided area such as an urban area, a suburb, or a highway.

  Here, the evaluation target data recorded in the database 18 is recorded in association with the area data specified by the latitude / longitude / azimuth of the vehicle received by the GPS receiver 324. With respect to speed, acceleration, etc., it is possible to identify in which area the data is traveling.

  FIG. 11 is a functional block diagram of the driving evaluation unit 20. As shown in this figure, the driving evaluation unit 20 includes an unsafe driving action extraction unit 201, a reference data calculation unit 202, a principal data calculation unit 203, an evaluation reference setting unit 204, an evaluation score calculation unit 205, And an evaluation score totaling unit 206. The unsafe driving action extraction unit 201 extracts data corresponding to unsafe driving action data set for each evaluation item. Further, the reference data calculation unit 202 calculates the distribution, variance, and average value (statistical data) of all drivers of evaluation target data necessary for the evaluation of each evaluation item.

  Further, the principal data calculation unit 203, for each evaluation item, the ratio of the unsafe driving behavior of the driver to be evaluated and the average value of the evaluation target data of the driver to be evaluated (hereinafter referred to as principal data). Is calculated. Further, the evaluation criterion setting unit 204 sets an evaluation criterion that is a criterion for determining an evaluation score for each evaluation item. Here, the evaluation criterion setting unit 204 sets the weight of the evaluation score. Details of the weight setting will be described later. The evaluation score calculation unit 205 is an evaluation criterion setting unit based on the relationship between the distribution and variance calculated by the reference data calculation unit 202 and the principal data calculated by the principal data calculation unit 203 for each evaluation item. Based on the evaluation criteria set in 204, a five-step evaluation is performed. Furthermore, the evaluation point totaling unit 206 totals the evaluation points (evaluation values) calculated by the evaluation point calculation unit 24.

  FIG. 12 is a table summarizing evaluation items and methods for deriving personal data. As shown in this table, the evaluation items related to turning left and right are as follows: (1) No sudden right or left turn (ratio of sudden right or left turn), (2) Sufficient deceleration before the right turn (3) Slowly turn the handle when turning right, (4) Sufficiently slow down before turning left, (5) Slowly turn the handle when turning left (6) The driver is not stepping on a sudden brake during a right or left turn (a sudden braking action during a right or left turn).

  The action of item (1) is that the unsafe driving action extraction unit 201 obtains data whose absolute value has exceeded a predetermined value from the data of the lateral acceleration at the time of “right turn” or “left turn” recorded in the database 18. It is extracted by extracting. Further, the principal data calculation unit 203 counts the number of actions of the item (1) and the number of “right turn” and “left turn”, and the number of times of “right turn” and “left turn” of the number of actions of the item (1). Is calculated (hereinafter referred to as the ratio of the sudden handle). Here, calculation data of the ratio of the sudden handle of all drivers is recorded in the database 18, and the reference data calculation unit 202 is based on the calculation data recorded in the database 18. Calculate the distribution, variance, and average of the percentage of handles.

  The principal data of item (2) is obtained by the principal data calculation unit 203 calculating the average value of the speed immediately before the “right turn” recorded in the database 18. Further, the reference data calculation unit 202 calculates the distribution, variance, and average value of the speed immediately before “right turn” of all drivers recorded in the database 18.

  The principal data of the item (3) is obtained by the principal data calculation unit 203 calculating an average value of the absolute values of the lateral acceleration at the time of “right turn” recorded in the database 18. In addition, the reference data calculation unit 202 calculates the distribution, variance, and average value of the absolute values of the lateral acceleration at the time of “right turn” of all the drivers recorded in the database 18.

  The principal data of item (4) is obtained by the principal data calculation unit 203 calculating the average value of the speed immediately before the “left turn” recorded in the database 18. Further, the reference data calculation unit 202 calculates the distribution, variance, and average value of the speed immediately before “left turn” of all drivers recorded in the database 18.

  The principal data of the item (5) is obtained by the principal data calculation unit 203 calculating the average value of the absolute values of the lateral acceleration at the time of “left turn” recorded in the database 18. In addition, the reference data calculation unit 202 calculates the distribution, variance, and average value of the absolute values of the lateral acceleration at the time of “left turn” of all drivers recorded in the database 18.

  The action of item (6) is that the unsafe driving action extraction unit 201 obtains data whose absolute value exceeds a predetermined value from the longitudinal acceleration data recorded in the database 18 at the time of “right turn” or “left turn”. It is extracted by extracting. Further, the principal data calculation unit 203 counts the number of actions of the item (6) and the number of “right turn” and “left turn”, and the number of times of “right turn” and “left turn” of the number of actions of the item (6). (Hereinafter referred to as the ratio of sudden braking during a right or left turn). Here, calculation data of the ratio of sudden braking during the right / left turn of all drivers is recorded in the database 18, and the reference data calculation unit 202 is based on the calculation data recorded in the database 18. The distribution, variance, and average value of the ratio of sudden braking during the driver's right / left turn are calculated.

  In addition, as an evaluation item regarding the course change, (7) not making a sudden change of course (the ratio of sudden course changes), (8) slowly turning the steering wheel when changing course on a general road (9) The steering wheel is turned slowly when changing the course on the expressway, (10) The course is not changed frequently, (11) The car is not suddenly accelerating / decelerating during the course change ( Rate of sudden acceleration / deceleration during the course change).

  The action of item (7) is that the unsafe driving action extraction unit 201 extracts data whose absolute value exceeds a predetermined value from the data of the lateral acceleration at the time of “change of course” recorded in the database 18. It is extracted by. Also, the principal data calculation unit 203 counts the number of actions of the item (7) and the number of “change of course”, and the ratio of the number of actions of the item (7) to the number of “change of course” (hereinafter, abrupt Calculated as a percentage of the course change). Here, the database 18 stores calculation data of the ratio of abrupt course changes of all drivers, and the reference data calculation unit 202 is based on the calculation data recorded in the database 18. The distribution, variance, and average value of the rate of sudden course change are calculated.

  The principal data of the item (8) is obtained by the principal data calculation unit 203 calculating the average value of the absolute values of the lateral acceleration at the time of “change of course” in the general road traveling recorded in the database 18. . Further, the reference data calculation unit 202 calculates the distribution, variance, and average value of the absolute value of the lateral acceleration at the time of “change of course” in the general road traveling of all the drivers recorded in the database 18.

  The principal data of the item (9) is obtained by the principal data calculation unit 203 calculating the average value of the absolute values of the lateral acceleration at the time of “change of course” in the highway traveling recorded in the database 18. . Further, the reference data calculation unit 202 calculates the distribution, variance, and average value of the absolute value of the lateral acceleration at the time of “change of course” in the highway traveling of all the drivers recorded in the database 18.

  For the principal data of item (10), the principal data calculation unit 203 counts the number of “track changes” and calculates the ratio of the number of times to the total travel distance (hereinafter referred to as the ratio of the course change). Here, the calculation data of the ratio of the course change of all the drivers is recorded in the database 18, and the reference data calculation unit 202 is based on the calculation data recorded in the database 18. The change ratio distribution, variance, and average value are calculated.

  The action of item (11) is that the unsafe driving action extraction unit 201 extracts data whose absolute value exceeds a predetermined value from the longitudinal acceleration data at the time of “change of course” recorded in the database 18. It is extracted by. Also, the principal data calculation unit 203 counts the number of actions of the item (11) and the number of “change of course”, and the ratio of the number of actions of the item (11) to the number of “change of course” (hereinafter referred to as the course). Calculate the ratio of sudden acceleration / deceleration during change). Here, calculation data of the rate of sudden acceleration / deceleration during the course change of all drivers is recorded in the database 18, and the reference data calculation unit 202 is based on the calculation data recorded in the database 18. Then, the distribution, variance, and average value of the ratio of sudden acceleration / deceleration during the course change of all drivers are calculated.

  In addition, the evaluation items related to accelerator operation are as follows: (12) No sudden accelerator operation (rate of sudden acceleration action), (13) Slowly stepping on accelerator when starting, (14) High-speed travel Occasionally, you can step on the accelerator slowly.

  The action of item (12) is that the unsafe driving action extraction unit 201 extracts data whose absolute value exceeds a predetermined value from the longitudinal acceleration data at the time of “acceleration” recorded in the database 18. Extracted. The principal data calculation unit 203 counts the number of actions of the item (12) and the number of times of “acceleration”, and the ratio of the number of actions of the item (12) to the number of times of “acceleration” (hereinafter referred to as rapid acceleration). Is calculated). Here, calculation data of the rapid acceleration ratio of all the drivers is recorded in the database 18, and the reference data calculation unit 202 is based on the calculation data recorded in the database 18. The distribution, variance and average value of the acceleration rate are calculated.

  The principal data of the item (13) is obtained by the principal data calculation unit 203 calculating the average value of the longitudinal acceleration at the time of “start” recorded in the database 18. Further, the reference data calculation unit 202 calculates the distribution, variance, and average value of the longitudinal acceleration at the time of “start” of all the drivers recorded in the database 18.

  For the personal data of item (14), the personal data calculation unit 203 calculates an average value of the longitudinal acceleration at the time of “acceleration” during high speed running (speed is 40 km / h or more) recorded in the database 18. Can be obtained. Further, the reference data calculation unit 202 calculates the distribution, variance, and average value of the longitudinal acceleration at the time of “acceleration” during high speed traveling of all the drivers recorded in the database 18.

  In addition, as evaluation items related to brake operation, (15) not suddenly braking (ratio of sudden deceleration), (16) gently braking on ordinary roads, (17) on expressways, For example, the brake is gently depressed, and (18) the vehicle is not suddenly braked during high-speed driving (the rate of sudden deceleration during high-speed driving).

  The action of item (15) is as follows: the unsafe driving action extraction unit 201 extracts data whose absolute value exceeds a predetermined value from the longitudinal acceleration data at the time of “deceleration” recorded in the database 18. Extracted. The principal data calculation unit 203 counts the number of actions of the item (15) and the number of times of “deceleration”, and the ratio of the number of actions of the item (15) to the number of times of “deceleration” (hereinafter referred to as sudden braking). Is calculated). Here, calculation data of the ratio of sudden braking of all drivers is recorded in the database 18, and the reference data calculation unit 202 is based on the calculation data recorded in the database 18. The distribution, variance, and average value of the brake ratio are calculated.

  The principal data of the item (16) is obtained by the principal data calculation unit 203 calculating an average value of the absolute values of the longitudinal acceleration at the time of “deceleration” on the general road recorded in the database 18. Further, the reference data calculation unit 202 calculates the distribution, variance, and average value of the absolute values of the longitudinal acceleration at the time of “deceleration” in the general road traveling of all the drivers recorded in the database 18.

  The principal data of the item (17) is obtained by the principal data calculation unit 203 calculating an average value of the absolute values of the longitudinal acceleration at the time of “deceleration” on the highway traveling recorded in the database 18. . Further, the reference data calculation unit 202 calculates the distribution, variance, and average value of the absolute values of the longitudinal acceleration at the time of “deceleration” in the highway traveling of all the drivers recorded in the database 18.

  The action of item (18) is the absolute value of the unsafe driving action extraction unit 201 from the longitudinal acceleration data during “deceleration” during high speed running (speed is 40 km / h or more) recorded in the database 18. Is extracted by extracting data that exceeds a predetermined value. Also, the principal data calculation unit 203 counts the number of actions of item (18) and the number of times of “deceleration” during high speed running, and the number of times of “deceleration” during high speed running of the number of actions of item (18). (Hereinafter referred to as the ratio of sudden braking during high speed driving). Here, calculation data of the ratio of sudden braking during high-speed traveling of all drivers is recorded in the database 18, and the reference data calculation unit 202 is based on the calculation data recorded in the database 18. The distribution, variance, and average value of the ratio of sudden braking during high-speed driving by the driver are calculated.

  Further, as evaluation items related to traveling speed, (19) traveling on a general road (city) at an appropriate speed, (20) traveling on a suburban road at an appropriate speed, and (21) an expressway. You are driving at an appropriate speed.

  The principal data of the item (19) is obtained by the principal data calculation unit 203 calculating the average value of the “speed” in the general road traveling recorded in the database 18. In addition, the reference data calculation unit 202 calculates the distribution, variance, and average value of the “speed” of general driving on all the drivers recorded in the database 18.

  The principal data of the item (20) is obtained by the principal data calculation unit 203 calculating the average value of the “speed” in the suburban road running recorded in the database 18. Further, the reference data calculation unit 202 calculates the distribution, variance, and average value of the “speed” of all the drivers recorded on the database 18 in the suburban road driving.

  The principal data of the item (21) is obtained by the principal data calculation unit 203 calculating an average value of the “speed” on the highway traveling recorded in the database 18. In addition, the reference data calculation unit 202 calculates the distribution, variance, and average value of the “speed” of all the drivers recorded on the database 18 on the expressway.

  FIG. 13 is a graph showing reference data used when the evaluation score calculation unit 205 performs five-level evaluation for each evaluation item. As shown in this graph, the reference data is the distribution and variance of the evaluation target data, and the evaluation point calculation unit 205 determines whether the principal data is located in the distribution of the reference data in five levels for each evaluation item. evaluate.

  Specifically, when the principal data is located within the range of 2σ centered on the average point of the distribution of the reference data, 3 points (standard points) are given as evaluation points, and within the range of σ outside this range 2 or 4 points are given as evaluation points when positioned at 1, and 1 or 5 points are given as evaluation points when located within the outer σ range. The reference data is a distribution of a large number of drivers having the same driving environment (for example, a region or a traveling area) with the driver to be evaluated.

  By the way, the evaluation score calculation unit 205 corrects the value σ for determining the evaluation score based on the weight set by the evaluation criterion setting unit 204. That is, the evaluation score calculation unit 205 changes the boundary of the evaluation points according to the weight set by the evaluation criterion setting unit 204. Thereby, the evaluation score may change depending on the weight set by the evaluation criterion setting unit 204.

  Here, the weight is provided with a weight related to a dangerous act (hereinafter referred to as dangerous action weight) and a weight related to a dangerous area (hereinafter referred to as dangerous area weight). The dangerous action weight is a weight applied to each evaluation item. As the dangerous action weight increases, the value σ that determines the evaluation point of the evaluation item decreases, and the evaluation point of the evaluation item easily decreases.

  The dangerous area weight is a weight applied to all evaluation items according to the traveling ratio of the traveling area (urban area, suburb, and expressway). For example, the suburb weight is 5 and the urban area and the expressway are separated. For example, when the suburban travel ratio is 60%, a weight of 3 is added to all evaluation items.

  Here, the initial values of these weights are set based on the analysis result of the occurrence state of the traffic accident, but are updated by the evaluation standard setting unit 204 every time a traffic accident occurs.

  FIG. 14 is a flowchart for explaining a weight update method by the evaluation criterion setting unit 204. As shown in this flowchart, first, in step 1, it is determined whether or not an update command is input from the input device 52. If an affirmative determination is made, the process proceeds to step 2. Here, in the driving evaluation device 10, when an evaluation subject has caused a traffic accident, the input device 52 can input the driver ID of the driver who has caused the traffic accident and the time of occurrence of the traffic accident. The driver ID and the measurement data are input to the main control unit 11 in a state in which the memory medium 40 in which the measurement data associated with the driver ID is recorded is attached to the safe driving evaluation device 10. Then, the weight update process by the evaluation criterion setting unit 204 is executed.

In step 2, the evaluation criterion setting unit 204 extracts the following items (1) to (3) corresponding to the traffic accident occurrence time input from the input device 52 from the database 18.
(1) Driving area (city area, suburb, highway, etc.), (2) Driving action (turn left and right, acceleration, etc.), (3) Presence of sudden operation (rapid steering wheel, sudden braking, etc.)

Next, in step 3, the evaluation criterion setting unit 204 calculates the following items (4) and (5) corresponding to the traffic accident input from the input device 52 based on the data extracted from the database 18. Here, the items (4) and (5) are for specifying whether or not there is room for avoiding an accident, and are derived based on the speed and acceleration in the front and rear, right and left directions.
(4) Overspeed with respect to speed at which collision could be avoided, (5) Delay time with respect to sudden deceleration start time at which collision could be avoided

  FIG. 15 is a graph showing the speed immediately before the collision and the longitudinal acceleration. As shown in this graph, the absolute value of the longitudinal acceleration becomes excessive (for example, about 3G) at the time of a collision. Further, it can be seen that “deceleration acceleration” increases (ie, “deceleration” starts) before the collision time, and that “deceleration acceleration” increases abruptly (ie, “sudden deceleration” starts). .

  Here, the time from the start of "deceleration" to the time of "collision" is the difference between the time when "deceleration" starts and the time when "collision" occurs. Can be calculated from the above time, “speed”, and “deceleration acceleration”. Then, based on this time, distance, speed, and deceleration acceleration, the speed at which the collision could be avoided when the same brake operation was performed, and the “sudden deceleration start” time at which the collision could be avoided when traveling at the same speed (Timing) and the like can be calculated.

  Therefore, when calculating the items (4) and (5), the evaluation criterion setting unit 204 first determines the time when the longitudinal acceleration exceeds a predetermined threshold (for example, the absolute value is 3G) (that is, the time of collision). The database 18 shows the “deceleration start” time immediately before that time, the “sudden deceleration start” time between the “collision” time and the “deceleration start” time, and the speed at the “deceleration start” time. Extract from Then, the evaluation criterion setting unit 204 calculates a difference (time) between the time when the “deceleration” starts and the time when the “collision” occurs, and between the difference and the “deceleration start” to the “collision”. Based on the speed and deceleration acceleration, the distance from “deceleration start” to “collision” is calculated. Based on the calculated time and distance from “deceleration start” to “collision” and “deceleration acceleration” therebetween, the evaluation criterion setting unit 204 can avoid the collision under the same deceleration condition. And the “sudden deceleration start” time at which the collision could be avoided under the same speed condition. Furthermore, the evaluation criterion setting unit 204 calculates the difference between the calculated speed and the actual speed (corresponding to the item (4)), and calculates the calculated “rapid deceleration start” time and the actual “rapid deceleration start” time. Difference (corresponding to item (5)) is calculated.

  Then, as shown in the flowchart of FIG. 14, in step 4, the evaluation criterion setting unit 204 adds the dangerous area weight of the traveling area extracted in item (1). Next, in step 5, the evaluation criterion setting unit 204 determines whether or not the past evaluation score in the evaluation item of the driver related to the action extracted in the items (2) and (3) is 2 or less. If the number is 2 or less, the process proceeds to Step 6. If the number is 3 or more, the process proceeds to Step 7. In step 6, the evaluation criterion setting unit 204 adds the dangerous action weights of the evaluation items related to the actions extracted in the items (2) and (3).

  In step 7, the evaluation criterion setting unit 204 determines whether or not the overspeed with respect to the speed at which the collision calculated in the item (4) can be avoided is equal to or greater than a predetermined threshold (whether excessive speed has contributed to the accident). If it is equal to or greater than a predetermined threshold value, the process proceeds to step 71. If it is less than the predetermined threshold value, the process proceeds to step 9. In step 71, it is determined whether or not the past evaluation score (items (19) to (21)) regarding the speed of the driver is 2 or less. If YES, go to step 9. In step 8, the evaluation criterion setting unit 204 adds the dangerous action weights of the evaluation items (the above items (19) to (21)) related to the traveling speed.

  In step 9, the evaluation criterion setting unit 204 determines whether or not the delay time with respect to the rapid deceleration start time at which the collision calculated in the item (5) can be avoided is equal to or greater than a predetermined threshold (determination is a cause of the accident). It is determined whether or not the threshold value is greater than or equal to a predetermined threshold value, the process proceeds to step 91. In step 91, whether or not the past evaluation score regarding the driver's "rapid deceleration rate (items (15), (18))" and "change of course while rapidly decelerating (item (11))" is 2 or less. If it is 2 or less, the process proceeds to step 10, and if it is 3 or more, the process proceeds to step 11. In step 10, the evaluation criterion setting unit 204 evaluates an evaluation item related to the rapid deceleration rate (described above). Items (15) and (18)) and the risk action weight of the evaluation item (the above item (11)) related to the course change while rapidly decelerating are added.

In step 11, the evaluation criterion setting unit 204 determines the following item (6) based on data extracted from the database 18.
(6) Whether the accident is immediately after a right or left turn

  In this step, the evaluation criterion setting unit 204 determines whether “right turn” or “left turn” has been extracted immediately before the collision. If it has been extracted, the process proceeds to step 12; Then, the process ends. In step 12, the evaluation criterion setting unit 204 adds the dangerous action weights of the evaluation items (the above items (2) and (4)) related to sufficient deceleration before “right turn” or “left turn”.

  FIG. 16 shows a report of results regarding the safe driving technique of the driver to be evaluated. In the form shown in this figure, personal data, reference data (average value of all drivers), and evaluation points are described for each of the evaluation items (1) to (21). The form also describes the total score derived by totaling the evaluation points of each evaluation item. This total score is the one calculated by the evaluation score totaling unit 206.

  Here, evaluation points are not given for all evaluation items. For example, in the case where highway driving has never been performed, evaluation points for highway driving are not given. Therefore, the evaluation score totaling unit 206 calculates the ratio of the total value of the evaluation points to the total value of the full scores of the evaluation items given the evaluation points as a total score.

  As described above, in the driving evaluation system 100 according to the present embodiment, the relationship between the evaluation target data such as the acceleration at the time of act for the driver to be evaluated and the statistical data of the evaluation target data for a plurality of drivers. The driver of the evaluation object was evaluated according to a predetermined evaluation standard based on the above. Thus, for example, a large number of drivers having a common driving environment are used as a population, and the driving of the driver is evaluated based on where the driving action of the driver to be evaluated is located in the population. be able to. Therefore, rather than evaluating driving based on an absolute evaluation criterion, more appropriate evaluation can be performed by evaluating driving based on a relative evaluation criterion considering relativity with other drivers.

  Further, in the driving evaluation system 100 according to the present embodiment, the dangerous area weight is changed according to the traveling ratio for each specific area such as an urban area, a suburb, and an expressway of the driver to be evaluated, and the evaluation criteria of each evaluation item Has changed. As a result, it is possible to perform more appropriate evaluation in consideration of the driving environment of each driver, such as strict evaluation for a driver with a high driving ratio in an area where an accident is likely to occur.

  Further, in the driving evaluation system 100 according to the present embodiment, when the driver to be evaluated causes a traffic accident, the driving action causing the accident is specified and the dangerous action weight of the evaluation item related to the driving action is determined. The evaluation criteria for the relevant evaluation items are updated. In addition, referring to the evaluation target data for the driver, if the evaluation item for the driving action that caused the accident is bad, the risky weight of the evaluation item is increased, and the evaluation criteria for the evaluation item are set. It has been updated. In other words, in the driving evaluation system 100 according to the present embodiment, the evaluation criteria for each evaluation item is updated each time a traffic accident occurs for a driver who receives a driving evaluation service. Therefore, a more appropriate evaluation can be performed by reflecting the analysis result of the traffic accident in the evaluation criteria as needed.

  Further, in the driving evaluation system 100 according to the present embodiment, it is divided into seven specific driving actions such as start, acceleration, deceleration, stop, right turn, left turn, and course change, and each action is further divided into speed and operation strength. The evaluation items were set, and the evaluation points of each evaluation item were totaled to score the driving of the driver. Thereby, a driver | operator's fault can be grasped | ascertained separately concretely, and not only evaluation of a driver | operator's driving | operation but a driver | operator's guidance can be performed effectively.

  FIG. 17 is a functional block diagram of the driving evaluation unit 120 according to another embodiment. FIG. 18 is a diagram schematically illustrating a method for calculating the stability factor. As illustrated in FIG. 17, the driving evaluation unit 120 includes a reference data calculation unit 122, an evaluation target data determination unit 125, and a stability rate calculation unit 126.

  As shown in FIGS. 17 and 18, the reference data calculation unit 122 calculates the distribution and variance of each evaluation target data of all the drivers stored in the database 18. Further, the evaluation target data determination unit 125 includes each evaluation target data of the evaluation target driver extracted by the numerical value extraction unit 14, and distribution of each evaluation target data of all the drivers calculated by the reference data calculation unit 122. Whether or not each evaluation target data of the driver to be evaluated is out of the standard range of the distribution of the evaluation target data of all drivers (corresponding to the range of the above-mentioned evaluation point 3). Determine.

  Then, the stability factor calculation unit 126 is evaluated by the evaluation target data determination unit 125 and the number of evaluation target data of the evaluation target driver determined to be out of the standard range of all drivers. A ratio with the total number of evaluation target data of the target driver is calculated. Here, the ratio calculated by the stability factor calculation unit 126 corresponds to the ratio of driving behavior outside the average to the total driving behavior of the driver to be evaluated, and this ratio is the driving stability factor of the driver. This stability factor can be regarded as a numerical value indicating the stability of the driver's attention.

  As described above, in the driving evaluation system according to the present embodiment, the quality of each driving action of the driver to be evaluated is determined based on a predetermined evaluation criterion (that is, whether or not the driving action is out of average). A ratio of driving actions determined to be out of average driving actions to all driving actions is calculated. As a result, the driving stability rate of the driver can be evaluated, and the stability of the driver's attention can be evaluated.

  FIG. 19 is a block diagram illustrating a schematic configuration of a driving evaluation system 200 according to another embodiment. The driving evaluation system 200 includes a driving evaluation device 210 and a constantly-recording type data recorder 230 mounted on a mobile device 201 such as a touch panel type mobile phone. The driving evaluation apparatus 210 and the mobile device 201 on which the data recorder 230 is mounted are connected by a mobile data communication network.

  The data recorder 230 includes a sensor unit 32 similar to the data recorder 30 described above. In addition, the mobile device 201 includes a data communication unit 234 that performs data communication with the driving evaluation device 210. Application software is installed in the mobile device 201 in order to activate the data recorder 230 and perform data communication with the driving evaluation apparatus 210. When the application software is started up, the data recorder 230 is activated and data communication is performed. Data measured by the sensor unit 32 is transmitted from the unit 234 to the driving evaluation device 210, or a warning signal described later is transmitted from the driving evaluation device 210 to the data communication unit 234.

  FIG. 20 is a block diagram illustrating a configuration of the driving evaluation apparatus 210. As shown in this figure, the driving evaluation device 210 includes a data communication unit 251 that performs data communication with the data communication unit 234 of the mobile device 201, an input device 52, a display device 53, and a main control unit 211. Yes. The main control unit 211 includes a driving action extraction unit 12, a numerical value extraction unit 14, a data recording unit 16, a database 18, a driving evaluation unit 20, and an evaluation information management unit 222.

  The driving action extraction unit 12 executes a process of extracting the driving action from the measurement data received by the data communication unit 251. Further, the data recording unit 16 links the driver ID received by the data communication unit 251 and the evaluation target data extracted by the numerical value extraction unit 14 and records them in the database 18. Here, the processing of the driving action extraction unit 12, the numerical value extraction unit 14, and the data recording unit 16 is executed at a predetermined cycle (for example, 1 Hz) while the vehicle is traveling.

  The driving evaluation unit 20 periodically obtains the overall distribution of the evaluation target data of all the drivers accumulated in the database 18, and based on the total distribution and the personal data, the safety of the driver to be evaluated Evaluate driving skills.

  The evaluation information management unit 222 creates image data for recording the data of the evaluation result by the driving evaluation unit 20 in the database 18 and for creating a form that summarizes the results of the safe driving of the driver to be evaluated. Or In addition, the evaluation information management unit 222 determines whether the evaluation result is good or bad and generates a warning signal based on the determination result. The form image data and the warning signal are transmitted from the data communication unit 251 to the data communication unit 234 of the mobile device 201. Then, a form image or a warning image is displayed on the display screen of the mobile device 201, or a warning sound is emitted from the speaker of the mobile device 201.

  As described above, in the driving evaluation system 200 according to the present embodiment, data communication between the data recorder 230 and the driving evaluation device 210 can be performed in real time while the vehicle is traveling via the mobile data communication network. Therefore, it is possible to warn the driver in operation in real time or notify the evaluation.

  In addition, the above-mentioned embodiment is for making an understanding of this invention easy, and does not limit this invention. It goes without saying that the present invention can be changed and improved without departing from the gist thereof, and that the present invention includes equivalents thereof.

  For example, in the above-described embodiment, the average value of the evaluation target data for the driver to be evaluated is the principal data. However, a value obtained by multiplying the average value of the evaluation target data for the driver to be evaluated and the deviation, etc. What is necessary is just to select suitably the calculation methods of principal data, such as setting it as principal data. Further, in the above-described embodiment, the distribution of the evaluation target data of all the drivers is statistical data, and the driving of the evaluation target driver is evaluated depending on where the principal data is located in the distribution of the evaluation target data of all the drivers. did. However, the average value of the evaluation target data of all drivers may be used as reference data, and the driving of the evaluation target driver may be evaluated by comparing this with the personal data.

DESCRIPTION OF SYMBOLS 10 Driving evaluation apparatus, 11 Main control part, 12 Driving action extraction part (traveling behavior specific part), 14 Numerical value extraction part (data extraction part), 16 Database (data recording part), 18 Data recording part, 20 Driving evaluation part, 201 unsafe driving action extraction unit, 202 reference data calculation unit, 203 identity data calculation unit, 204 evaluation standard setting unit, 205 evaluation point calculation unit (evaluation value calculation unit), 206 evaluation point totaling unit (evaluation value totaling unit), 22 evaluation information management unit, 30 data recorder, 32 sensor unit, 321 angular velocity sensor, 322 lateral acceleration sensor, 323 longitudinal acceleration sensor, 324 GPS receiver, 325 vehicle speed pulse acquisition unit, 34 data recording unit, 341 preprocessing unit, 342 data Recording unit, 343 buffer memory, 40 memory medium, 51 memory control unit (data acquisition unit), 52 input device, 53 display device, 100 driving evaluation system, 120 driving evaluation unit, 122 reference data calculation unit, 125 extraction data determination unit (good / bad determination unit), 126 stability factor calculation unit (ratio calculation unit), 200 driving evaluation system , 201 Mobile device, 210 Driving evaluation device, 211 Main control unit, 222 Evaluation information management unit, 230 Data recorder, 234 Data communication unit, 251 Data communication unit (data acquisition unit)

Claims (8)

A driving evaluation system that evaluates driving of a vehicle driver based on driving behavior data collected in time series during operation of the vehicle to identify driving behavior of the vehicle,
A data acquisition unit for acquiring the driving behavior data;
Based on the driving behavior data acquired by the data acquisition unit, a driving behavior specifying unit that specifies the driving behavior of the vehicle,
A data extraction unit for extracting evaluation target data which is the traveling behavior data related to the traveling behavior of the vehicle identified by the traveling behavior identifying unit;
A data recording unit for recording the evaluation target data for a plurality of drivers including the driver of the evaluation target extracted by the data extraction unit;
Statistical data of the evaluation target data for the plurality of drivers recorded in the data recording unit is calculated, and the evaluation target data for the evaluation target driver recorded in the data recording unit, Based on the relationship with the statistical data, according to a predetermined evaluation criteria, a driving evaluation unit that evaluates the driving of the driver to be evaluated,
A driving evaluation system comprising: The data acquisition unit acquires travel area data for identifying the travel area of the vehicle collected in time series together with the travel behavior data during operation of the vehicle,
The driving evaluation unit
An evaluation standard setting unit that calculates a travel ratio of the travel area of the driver to be evaluated from the travel area data acquired by the data acquisition unit, and sets the predetermined evaluation standard according to the calculated travel ratio When,
An evaluation value calculation unit that calculates an evaluation value according to the predetermined evaluation criterion set by the evaluation criterion setting unit;
The driving | running evaluation system of Claim 1 provided with. The evaluation criteria setting unit
When the evaluation target driver is a driver who has caused a traffic accident, the predetermined evaluation criterion is set based on the evaluation target data for the driver recorded in the evaluation target data recording unit. The operation evaluation system according to claim 2.   The driving evaluation system according to claim 2 or 3, wherein the evaluation criterion setting unit sets a weight value used when the evaluation value calculation unit calculates an evaluation value when the predetermined evaluation criterion is set. The evaluation criterion setting unit sets the predetermined evaluation criterion for each evaluation item set for each of a plurality of driving actions,
The evaluation value calculation unit calculates the evaluation value for each evaluation item set for each of a plurality of driving actions,
The driving evaluation unit
An evaluation value totaling unit that totals the evaluation values of a plurality of evaluation items calculated by the evaluation value calculation unit and scores the driving of the evaluation target driver based on the total value. The driving evaluation system according to any one of claims 4 to 4. The driving evaluation unit
A pass / fail determination unit that determines pass / fail of each driving action of the driver to be evaluated according to the predetermined evaluation criteria;
A ratio calculation unit that calculates a ratio of the driving action that is negatively determined by the pass / fail determination part to the total driving action;
The driving | running evaluation system of Claim 1 provided with. Driving evaluation to enable a computer to implement a function for evaluating the driving of a vehicle driver based on driving behavior data collected in time series during operation of the vehicle to identify the driving behavior of the vehicle A program for
A data acquisition function for acquiring the driving behavior data;
Based on the driving behavior data acquired by the data acquisition function, a driving behavior specifying function for specifying the driving behavior of the vehicle,
A data extraction function for extracting evaluation target data which is the traveling behavior data related to the traveling behavior of the vehicle identified by the traveling behavior identifying function;
A data recording function for recording the evaluation target data for a plurality of drivers including the driver of the evaluation target extracted by the data extraction function;
Calculate statistical data of the evaluation target data for the plurality of drivers recorded by the data recording function, the evaluation target data for the evaluation target driver recorded by the data recording function, and A driving evaluation function for evaluating the driving of the driver to be evaluated according to a predetermined evaluation criterion based on a relationship with statistical data;
A program for driving evaluation to realize a computer. A driving evaluation method in which a procedure for evaluating driving of a vehicle driver is executed by a computer based on driving behavior data collected in time series during operation of the vehicle to identify the driving behavior of the vehicle. There,
A data acquisition procedure for acquiring the driving behavior data;
Based on the driving behavior data acquired in the data acquisition procedure, a driving behavior specifying procedure for specifying the driving behavior of the vehicle,
A data extraction procedure for extracting evaluation target data that is the travel behavior data related to the travel behavior of the vehicle identified in the travel behavior identification procedure;
A data recording procedure for recording the evaluation target data for a plurality of drivers including the driver of the evaluation target extracted in the data extraction procedure in a recording unit;
Statistical data of the evaluation target data for the plurality of drivers recorded by the data recording procedure is calculated, the evaluation target data for the evaluation target driver recorded by the data recording procedure, and the statistics A driving evaluation procedure for evaluating the driving of the driver to be evaluated according to a predetermined evaluation criterion based on a relationship with data;
A driving evaluation method that runs on a computer.